Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations27312
Missing cells94165
Missing cells (%)16.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.2 MiB
Average record size in memory161.0 B

Variable types

Numeric6
DateTime2
Categorical11
Boolean1
Text1

Alerts

BORO is highly overall correlated with Latitude and 4 other fieldsHigh correlation
INCIDENT_KEY is highly overall correlated with LOC_CLASSFCTN_DESC and 1 other fieldsHigh correlation
JURISDICTION_CODE is highly overall correlated with LOCATION_DESC and 1 other fieldsHigh correlation
LOCATION_DESC is highly overall correlated with JURISDICTION_CODE and 1 other fieldsHigh correlation
LOC_CLASSFCTN_DESC is highly overall correlated with INCIDENT_KEY and 3 other fieldsHigh correlation
LOC_OF_OCCUR_DESC is highly overall correlated with INCIDENT_KEY and 1 other fieldsHigh correlation
Latitude is highly overall correlated with BORO and 2 other fieldsHigh correlation
Longitude is highly overall correlated with BORO and 1 other fieldsHigh correlation
PERP_AGE_GROUP is highly overall correlated with PERP_SEXHigh correlation
PERP_RACE is highly overall correlated with PERP_SEXHigh correlation
PERP_SEX is highly overall correlated with PERP_AGE_GROUP and 1 other fieldsHigh correlation
PRECINCT is highly overall correlated with BORO and 2 other fieldsHigh correlation
X_COORD_CD is highly overall correlated with BORO and 1 other fieldsHigh correlation
Y_COORD_CD is highly overall correlated with BORO and 2 other fieldsHigh correlation
JURISDICTION_CODE is highly imbalanced (58.0%) Imbalance
PERP_SEX is highly imbalanced (60.6%) Imbalance
VIC_SEX is highly imbalanced (71.0%) Imbalance
VIC_RACE is highly imbalanced (52.5%) Imbalance
LOC_OF_OCCUR_DESC has 25596 (93.7%) missing values Missing
LOC_CLASSFCTN_DESC has 25596 (93.7%) missing values Missing
LOCATION_DESC has 14977 (54.8%) missing values Missing
PERP_AGE_GROUP has 9344 (34.2%) missing values Missing
PERP_SEX has 9310 (34.1%) missing values Missing
PERP_RACE has 9310 (34.1%) missing values Missing

Reproduction

Analysis started2025-05-28 16:31:48.611369
Analysis finished2025-05-28 16:32:02.149235
Duration13.54 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

INCIDENT_KEY
Real number (ℝ)

High correlation 

Distinct21420
Distinct (%)78.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2086054 × 108
Minimum9953245
Maximum2.6119019 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size213.5 KiB
2025-05-28T22:02:02.300749image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum9953245
5-th percentile24224969
Q163860880
median90372218
Q31.8881023 × 108
95-th percentile2.4266957 × 108
Maximum2.6119019 × 108
Range2.5123694 × 108
Interquartile range (IQR)1.2494935 × 108

Descriptive statistics

Standard deviation73412859
Coefficient of variation (CV)0.60741795
Kurtosis-1.2470904
Mean1.2086054 × 108
Median Absolute Deviation (MAD)55642069
Skewness0.34508435
Sum3.300943 × 1012
Variance5.3894478 × 1015
MonotonicityNot monotonic
2025-05-28T22:02:02.507028image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
173354054 18
 
0.1%
94424905 12
 
< 0.1%
23749375 12
 
< 0.1%
138997051 12
 
< 0.1%
215776333 12
 
< 0.1%
212640102 12
 
< 0.1%
72195829 12
 
< 0.1%
24717013 12
 
< 0.1%
79378503 12
 
< 0.1%
33706902 12
 
< 0.1%
Other values (21410) 27186
99.5%
ValueCountFrequency (%)
9953245 1
< 0.1%
9953246 1
< 0.1%
9953247 1
< 0.1%
9953248 1
< 0.1%
9953249 1
< 0.1%
9953250 2
< 0.1%
9953252 1
< 0.1%
9953255 2
< 0.1%
9953257 1
< 0.1%
9953258 1
< 0.1%
ValueCountFrequency (%)
261190187 1
 
< 0.1%
261176930 1
 
< 0.1%
261176929 1
 
< 0.1%
261175635 1
 
< 0.1%
261160236 1
 
< 0.1%
261120108 2
< 0.1%
261120017 1
 
< 0.1%
260637443 4
< 0.1%
260637442 1
 
< 0.1%
260531774 1
 
< 0.1%
Distinct5761
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Memory size213.5 KiB
Minimum2006-01-01 00:00:00
Maximum2022-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-05-28T22:02:02.712650image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-05-28T22:02:02.899020image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1421
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size213.5 KiB
Minimum2025-05-28 00:00:00
Maximum2025-05-28 23:59:00
Invalid dates0
Invalid dates (%)0.0%
2025-05-28T22:02:03.089360image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-05-28T22:02:03.284726image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

BORO
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size213.5 KiB
BROOKLYN
10933 
BRONX
7937 
QUEENS
4094 
MANHATTAN
3572 
STATEN ISLAND
 
776

Length

Max length13
Median length9
Mean length7.1012376
Min length5

Characters and Unicode

Total characters193949
Distinct characters19
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowQUEENS
2nd rowBRONX
3rd rowQUEENS
4th rowBRONX
5th rowBRONX

Common Values

ValueCountFrequency (%)
BROOKLYN 10933
40.0%
BRONX 7937
29.1%
QUEENS 4094
 
15.0%
MANHATTAN 3572
 
13.1%
STATEN ISLAND 776
 
2.8%

Length

2025-05-28T22:02:03.516232image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-28T22:02:03.698882image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
brooklyn 10933
38.9%
bronx 7937
28.3%
queens 4094
 
14.6%
manhattan 3572
 
12.7%
staten 776
 
2.8%
island 776
 
2.8%

Most occurring characters

ValueCountFrequency (%)
N 31660
16.3%
O 29803
15.4%
B 18870
9.7%
R 18870
9.7%
A 12268
 
6.3%
L 11709
 
6.0%
K 10933
 
5.6%
Y 10933
 
5.6%
E 8964
 
4.6%
T 8696
 
4.5%
Other values (9) 31243
16.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 193173
99.6%
Space Separator 776
 
0.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 31660
16.4%
O 29803
15.4%
B 18870
9.8%
R 18870
9.8%
A 12268
 
6.4%
L 11709
 
6.1%
K 10933
 
5.7%
Y 10933
 
5.7%
E 8964
 
4.6%
T 8696
 
4.5%
Other values (8) 30467
15.8%
Space Separator
ValueCountFrequency (%)
776
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 193173
99.6%
Common 776
 
0.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 31660
16.4%
O 29803
15.4%
B 18870
9.8%
R 18870
9.8%
A 12268
 
6.4%
L 11709
 
6.1%
K 10933
 
5.7%
Y 10933
 
5.7%
E 8964
 
4.6%
T 8696
 
4.5%
Other values (8) 30467
15.8%
Common
ValueCountFrequency (%)
776
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 193949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 31660
16.3%
O 29803
15.4%
B 18870
9.7%
R 18870
9.7%
A 12268
 
6.3%
L 11709
 
6.0%
K 10933
 
5.6%
Y 10933
 
5.6%
E 8964
 
4.6%
T 8696
 
4.5%
Other values (9) 31243
16.1%

LOC_OF_OCCUR_DESC
Categorical

High correlation  Missing 

Distinct2
Distinct (%)0.1%
Missing25596
Missing (%)93.7%
Memory size213.5 KiB
OUTSIDE
1474 
INSIDE
242 

Length

Max length7
Median length7
Mean length6.8589744
Min length6

Characters and Unicode

Total characters11770
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOUTSIDE
2nd rowOUTSIDE
3rd rowOUTSIDE
4th rowOUTSIDE
5th rowOUTSIDE

Common Values

ValueCountFrequency (%)
OUTSIDE 1474
 
5.4%
INSIDE 242
 
0.9%
(Missing) 25596
93.7%

Length

2025-05-28T22:02:03.881994image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-28T22:02:04.015855image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
outside 1474
85.9%
inside 242
 
14.1%

Most occurring characters

ValueCountFrequency (%)
I 1958
16.6%
S 1716
14.6%
D 1716
14.6%
E 1716
14.6%
T 1474
12.5%
U 1474
12.5%
O 1474
12.5%
N 242
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 11770
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 1958
16.6%
S 1716
14.6%
D 1716
14.6%
E 1716
14.6%
T 1474
12.5%
U 1474
12.5%
O 1474
12.5%
N 242
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 11770
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 1958
16.6%
S 1716
14.6%
D 1716
14.6%
E 1716
14.6%
T 1474
12.5%
U 1474
12.5%
O 1474
12.5%
N 242
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11770
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 1958
16.6%
S 1716
14.6%
D 1716
14.6%
E 1716
14.6%
T 1474
12.5%
U 1474
12.5%
O 1474
12.5%
N 242
 
2.1%

PRECINCT
Real number (ℝ)

High correlation 

Distinct77
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.635362
Minimum1
Maximum123
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size213.5 KiB
2025-05-28T22:02:04.165879image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile25
Q144
median68
Q381
95-th percentile113
Maximum123
Range122
Interquartile range (IQR)37

Descriptive statistics

Standard deviation27.305705
Coefficient of variation (CV)0.41602125
Kurtosis-0.73292236
Mean65.635362
Median Absolute Deviation (MAD)22
Skewness0.21090762
Sum1792633
Variance745.60154
MonotonicityNot monotonic
2025-05-28T22:02:04.349143image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75 1557
 
5.7%
73 1452
 
5.3%
67 1216
 
4.5%
44 1020
 
3.7%
79 1012
 
3.7%
47 953
 
3.5%
40 908
 
3.3%
46 895
 
3.3%
42 850
 
3.1%
113 802
 
2.9%
Other values (67) 16647
61.0%
ValueCountFrequency (%)
1 25
 
0.1%
5 58
0.2%
6 28
 
0.1%
7 109
0.4%
9 109
0.4%
10 73
0.3%
13 60
0.2%
14 56
0.2%
17 10
 
< 0.1%
18 34
 
0.1%
ValueCountFrequency (%)
123 31
 
0.1%
122 61
 
0.2%
121 112
 
0.4%
120 572
2.1%
115 179
 
0.7%
114 369
1.4%
113 802
2.9%
112 23
 
0.1%
111 11
 
< 0.1%
110 160
 
0.6%

JURISDICTION_CODE
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size213.5 KiB
0.0
22809 
2.0
4427 
1.0
 
74

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters81930
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 22809
83.5%
2.0 4427
 
16.2%
1.0 74
 
0.3%
(Missing) 2
 
< 0.1%

Length

2025-05-28T22:02:04.515906image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-28T22:02:04.649279image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 22809
83.5%
2.0 4427
 
16.2%
1.0 74
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 50119
61.2%
. 27310
33.3%
2 4427
 
5.4%
1 74
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 54620
66.7%
Other Punctuation 27310
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 50119
91.8%
2 4427
 
8.1%
1 74
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 27310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 81930
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 50119
61.2%
. 27310
33.3%
2 4427
 
5.4%
1 74
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 81930
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 50119
61.2%
. 27310
33.3%
2 4427
 
5.4%
1 74
 
0.1%

LOC_CLASSFCTN_DESC
Categorical

High correlation  Missing 

Distinct9
Distinct (%)0.5%
Missing25596
Missing (%)93.7%
Memory size213.5 KiB
STREET
1103 
HOUSING
280 
DWELLING
127 
COMMERCIAL
 
100
OTHER
 
31
Other values (4)
 
75

Length

Max length11
Median length6
Mean length6.6386946
Min length5

Characters and Unicode

Total characters11392
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSTREET
2nd rowSTREET
3rd rowSTREET
4th rowSTREET
5th rowSTREET

Common Values

ValueCountFrequency (%)
STREET 1103
 
4.0%
HOUSING 280
 
1.0%
DWELLING 127
 
0.5%
COMMERCIAL 100
 
0.4%
OTHER 31
 
0.1%
PLAYGROUND 30
 
0.1%
VEHICLE 23
 
0.1%
TRANSIT 15
 
0.1%
PARKING LOT 7
 
< 0.1%
(Missing) 25596
93.7%

Length

2025-05-28T22:02:04.807860image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-28T22:02:04.974667image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
street 1103
64.0%
housing 280
 
16.3%
dwelling 127
 
7.4%
commercial 100
 
5.8%
other 31
 
1.8%
playground 30
 
1.7%
vehicle 23
 
1.3%
transit 15
 
0.9%
parking 7
 
0.4%
lot 7
 
0.4%

Most occurring characters

ValueCountFrequency (%)
E 2510
22.0%
T 2274
20.0%
S 1398
12.3%
R 1286
11.3%
I 552
 
4.8%
N 459
 
4.0%
O 448
 
3.9%
G 444
 
3.9%
L 414
 
3.6%
H 334
 
2.9%
Other values (11) 1273
11.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 11385
99.9%
Space Separator 7
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 2510
22.0%
T 2274
20.0%
S 1398
12.3%
R 1286
11.3%
I 552
 
4.8%
N 459
 
4.0%
O 448
 
3.9%
G 444
 
3.9%
L 414
 
3.6%
H 334
 
2.9%
Other values (10) 1266
11.1%
Space Separator
ValueCountFrequency (%)
7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11385
99.9%
Common 7
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 2510
22.0%
T 2274
20.0%
S 1398
12.3%
R 1286
11.3%
I 552
 
4.8%
N 459
 
4.0%
O 448
 
3.9%
G 444
 
3.9%
L 414
 
3.6%
H 334
 
2.9%
Other values (10) 1266
11.1%
Common
ValueCountFrequency (%)
7
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11392
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 2510
22.0%
T 2274
20.0%
S 1398
12.3%
R 1286
11.3%
I 552
 
4.8%
N 459
 
4.0%
O 448
 
3.9%
G 444
 
3.9%
L 414
 
3.6%
H 334
 
2.9%
Other values (11) 1273
11.2%

LOCATION_DESC
Categorical

High correlation  Missing 

Distinct40
Distinct (%)0.3%
Missing14977
Missing (%)54.8%
Memory size213.5 KiB
MULTI DWELL - PUBLIC HOUS
4832 
MULTI DWELL - APT BUILD
2835 
(null)
977 
PVT HOUSE
951 
GROCERY/BODEGA
694 
Other values (35)
2046 

Length

Max length25
Median length23
Mean length19.263802
Min length3

Characters and Unicode

Total characters237619
Distinct characters33
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.1%

Sample

1st rowMULTI DWELL - APT BUILD
2nd rowMULTI DWELL - PUBLIC HOUS
3rd rowGROCERY/BODEGA
4th rowMULTI DWELL - PUBLIC HOUS
5th rowMULTI DWELL - PUBLIC HOUS

Common Values

ValueCountFrequency (%)
MULTI DWELL - PUBLIC HOUS 4832
 
17.7%
MULTI DWELL - APT BUILD 2835
 
10.4%
(null) 977
 
3.6%
PVT HOUSE 951
 
3.5%
GROCERY/BODEGA 694
 
2.5%
BAR/NIGHT CLUB 628
 
2.3%
COMMERCIAL BLDG 292
 
1.1%
RESTAURANT/DINER 204
 
0.7%
NONE 175
 
0.6%
BEAUTY/NAIL SALON 112
 
0.4%
Other values (30) 635
 
2.3%
(Missing) 14977
54.8%

Length

2025-05-28T22:02:05.182475image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
multi 7667
16.8%
dwell 7667
16.8%
7667
16.8%
public 4832
10.6%
hous 4832
10.6%
apt 2835
 
6.2%
build 2835
 
6.2%
null 977
 
2.1%
pvt 951
 
2.1%
house 951
 
2.1%
Other values (50) 4344
9.5%

Most occurring characters

ValueCountFrequency (%)
33223
14.0%
L 32783
13.8%
U 22312
 
9.4%
I 17099
 
7.2%
T 13357
 
5.6%
D 11934
 
5.0%
E 11525
 
4.9%
B 10110
 
4.3%
P 8788
 
3.7%
O 8669
 
3.6%
Other values (23) 67819
28.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 189067
79.6%
Space Separator 33223
 
14.0%
Dash Punctuation 7667
 
3.2%
Lowercase Letter 3908
 
1.6%
Other Punctuation 1800
 
0.8%
Open Punctuation 977
 
0.4%
Close Punctuation 977
 
0.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L 32783
17.3%
U 22312
11.8%
I 17099
 
9.0%
T 13357
 
7.1%
D 11934
 
6.3%
E 11525
 
6.1%
B 10110
 
5.3%
P 8788
 
4.6%
O 8669
 
4.6%
M 8408
 
4.4%
Other values (14) 44082
23.3%
Lowercase Letter
ValueCountFrequency (%)
l 1954
50.0%
u 977
25.0%
n 977
25.0%
Other Punctuation
ValueCountFrequency (%)
/ 1789
99.4%
. 11
 
0.6%
Space Separator
ValueCountFrequency (%)
33223
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7667
100.0%
Open Punctuation
ValueCountFrequency (%)
( 977
100.0%
Close Punctuation
ValueCountFrequency (%)
) 977
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 192975
81.2%
Common 44644
 
18.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 32783
17.0%
U 22312
11.6%
I 17099
 
8.9%
T 13357
 
6.9%
D 11934
 
6.2%
E 11525
 
6.0%
B 10110
 
5.2%
P 8788
 
4.6%
O 8669
 
4.5%
M 8408
 
4.4%
Other values (17) 47990
24.9%
Common
ValueCountFrequency (%)
33223
74.4%
- 7667
 
17.2%
/ 1789
 
4.0%
( 977
 
2.2%
) 977
 
2.2%
. 11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 237619
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
33223
14.0%
L 32783
13.8%
U 22312
 
9.4%
I 17099
 
7.2%
T 13357
 
5.6%
D 11934
 
5.0%
E 11525
 
4.9%
B 10110
 
4.3%
P 8788
 
3.7%
O 8669
 
3.6%
Other values (23) 67819
28.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.8 KiB
False
22046 
True
5266 
ValueCountFrequency (%)
False 22046
80.7%
True 5266
 
19.3%
2025-05-28T22:02:05.316200image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

PERP_AGE_GROUP
Categorical

High correlation  Missing 

Distinct10
Distinct (%)0.1%
Missing9344
Missing (%)34.2%
Memory size213.5 KiB
18-24
6222 
25-44
5687 
UNKNOWN
3148 
<18
1591 
(null)
640 
Other values (5)
680 

Length

Max length7
Median length5
Mean length5.2019702
Min length3

Characters and Unicode

Total characters93469
Distinct characters21
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row25-44
2nd row25-44
3rd row25-44
4th rowUNKNOWN
5th rowUNKNOWN

Common Values

ValueCountFrequency (%)
18-24 6222
22.8%
25-44 5687
20.8%
UNKNOWN 3148
 
11.5%
<18 1591
 
5.8%
(null) 640
 
2.3%
45-64 617
 
2.3%
65+ 60
 
0.2%
940 1
 
< 0.1%
224 1
 
< 0.1%
1020 1
 
< 0.1%
(Missing) 9344
34.2%

Length

2025-05-28T22:02:05.490790image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-28T22:02:05.682365image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
18-24 6222
34.6%
25-44 5687
31.7%
unknown 3148
17.5%
18 1591
 
8.9%
null 640
 
3.6%
45-64 617
 
3.4%
65 60
 
0.3%
940 1
 
< 0.1%
224 1
 
< 0.1%
1020 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
4 18832
20.1%
- 12526
13.4%
2 11912
12.7%
N 9444
10.1%
1 7814
8.4%
8 7813
8.4%
5 6364
 
6.8%
U 3148
 
3.4%
K 3148
 
3.4%
O 3148
 
3.4%
Other values (11) 9320
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 53416
57.1%
Uppercase Letter 22036
23.6%
Dash Punctuation 12526
 
13.4%
Lowercase Letter 2560
 
2.7%
Math Symbol 1651
 
1.8%
Open Punctuation 640
 
0.7%
Close Punctuation 640
 
0.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 18832
35.3%
2 11912
22.3%
1 7814
14.6%
8 7813
14.6%
5 6364
 
11.9%
6 677
 
1.3%
0 3
 
< 0.1%
9 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 9444
42.9%
U 3148
 
14.3%
K 3148
 
14.3%
O 3148
 
14.3%
W 3148
 
14.3%
Lowercase Letter
ValueCountFrequency (%)
l 1280
50.0%
u 640
25.0%
n 640
25.0%
Math Symbol
ValueCountFrequency (%)
< 1591
96.4%
+ 60
 
3.6%
Dash Punctuation
ValueCountFrequency (%)
- 12526
100.0%
Open Punctuation
ValueCountFrequency (%)
( 640
100.0%
Close Punctuation
ValueCountFrequency (%)
) 640
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 68873
73.7%
Latin 24596
 
26.3%

Most frequent character per script

Common
ValueCountFrequency (%)
4 18832
27.3%
- 12526
18.2%
2 11912
17.3%
1 7814
11.3%
8 7813
11.3%
5 6364
 
9.2%
< 1591
 
2.3%
6 677
 
1.0%
( 640
 
0.9%
) 640
 
0.9%
Other values (3) 64
 
0.1%
Latin
ValueCountFrequency (%)
N 9444
38.4%
U 3148
 
12.8%
K 3148
 
12.8%
O 3148
 
12.8%
W 3148
 
12.8%
l 1280
 
5.2%
u 640
 
2.6%
n 640
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 93469
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 18832
20.1%
- 12526
13.4%
2 11912
12.7%
N 9444
10.1%
1 7814
8.4%
8 7813
8.4%
5 6364
 
6.8%
U 3148
 
3.4%
K 3148
 
3.4%
O 3148
 
3.4%
Other values (11) 9320
10.0%

PERP_SEX
Categorical

High correlation  Imbalance  Missing 

Distinct4
Distinct (%)< 0.1%
Missing9310
Missing (%)34.1%
Memory size213.5 KiB
M
15439 
U
 
1499
(null)
 
640
F
 
424

Length

Max length6
Median length1
Mean length1.177758
Min length1

Characters and Unicode

Total characters21202
Distinct characters8
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowU
5th rowU

Common Values

ValueCountFrequency (%)
M 15439
56.5%
U 1499
 
5.5%
(null) 640
 
2.3%
F 424
 
1.6%
(Missing) 9310
34.1%

Length

2025-05-28T22:02:05.882437image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-28T22:02:06.032455image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
m 15439
85.8%
u 1499
 
8.3%
null 640
 
3.6%
f 424
 
2.4%

Most occurring characters

ValueCountFrequency (%)
M 15439
72.8%
U 1499
 
7.1%
l 1280
 
6.0%
( 640
 
3.0%
n 640
 
3.0%
u 640
 
3.0%
) 640
 
3.0%
F 424
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 17362
81.9%
Lowercase Letter 2560
 
12.1%
Open Punctuation 640
 
3.0%
Close Punctuation 640
 
3.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 15439
88.9%
U 1499
 
8.6%
F 424
 
2.4%
Lowercase Letter
ValueCountFrequency (%)
l 1280
50.0%
n 640
25.0%
u 640
25.0%
Open Punctuation
ValueCountFrequency (%)
( 640
100.0%
Close Punctuation
ValueCountFrequency (%)
) 640
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19922
94.0%
Common 1280
 
6.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 15439
77.5%
U 1499
 
7.5%
l 1280
 
6.4%
n 640
 
3.2%
u 640
 
3.2%
F 424
 
2.1%
Common
ValueCountFrequency (%)
( 640
50.0%
) 640
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21202
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 15439
72.8%
U 1499
 
7.1%
l 1280
 
6.0%
( 640
 
3.0%
n 640
 
3.0%
u 640
 
3.0%
) 640
 
3.0%
F 424
 
2.0%

PERP_RACE
Categorical

High correlation  Missing 

Distinct8
Distinct (%)< 0.1%
Missing9310
Missing (%)34.1%
Memory size213.5 KiB
BLACK
11432 
WHITE HISPANIC
2341 
UNKNOWN
1836 
BLACK HISPANIC
1314 
(null)
 
640
Other values (3)
 
439

Length

Max length30
Median length5
Mean length7.2321409
Min length5

Characters and Unicode

Total characters130193
Distinct characters27
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBLACK
2nd rowBLACK
3rd rowBLACK
4th rowUNKNOWN
5th rowUNKNOWN

Common Values

ValueCountFrequency (%)
BLACK 11432
41.9%
WHITE HISPANIC 2341
 
8.6%
UNKNOWN 1836
 
6.7%
BLACK HISPANIC 1314
 
4.8%
(null) 640
 
2.3%
WHITE 283
 
1.0%
ASIAN / PACIFIC ISLANDER 154
 
0.6%
AMERICAN INDIAN/ALASKAN NATIVE 2
 
< 0.1%
(Missing) 9310
34.1%

Length

2025-05-28T22:02:06.203804image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-28T22:02:06.365815image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
black 12746
57.6%
hispanic 3655
 
16.5%
white 2624
 
11.9%
unknown 1836
 
8.3%
null 640
 
2.9%
asian 154
 
0.7%
154
 
0.7%
pacific 154
 
0.7%
islander 154
 
0.7%
american 2
 
< 0.1%
Other values (2) 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 17031
13.1%
C 16711
12.8%
K 14584
11.2%
L 12902
9.9%
B 12746
9.8%
I 10558
8.1%
N 9481
7.3%
H 6279
 
4.8%
W 4460
 
3.4%
4121
 
3.2%
Other values (17) 21320
16.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 122076
93.8%
Space Separator 4121
 
3.2%
Lowercase Letter 2560
 
2.0%
Open Punctuation 640
 
0.5%
Close Punctuation 640
 
0.5%
Other Punctuation 156
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 17031
14.0%
C 16711
13.7%
K 14584
11.9%
L 12902
10.6%
B 12746
10.4%
I 10558
8.6%
N 9481
7.8%
H 6279
 
5.1%
W 4460
 
3.7%
S 3965
 
3.2%
Other values (10) 13359
10.9%
Lowercase Letter
ValueCountFrequency (%)
l 1280
50.0%
n 640
25.0%
u 640
25.0%
Space Separator
ValueCountFrequency (%)
4121
100.0%
Open Punctuation
ValueCountFrequency (%)
( 640
100.0%
Close Punctuation
ValueCountFrequency (%)
) 640
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 156
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 124636
95.7%
Common 5557
 
4.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 17031
13.7%
C 16711
13.4%
K 14584
11.7%
L 12902
10.4%
B 12746
10.2%
I 10558
8.5%
N 9481
7.6%
H 6279
 
5.0%
W 4460
 
3.6%
S 3965
 
3.2%
Other values (13) 15919
12.8%
Common
ValueCountFrequency (%)
4121
74.2%
( 640
 
11.5%
) 640
 
11.5%
/ 156
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 130193
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 17031
13.1%
C 16711
12.8%
K 14584
11.2%
L 12902
9.9%
B 12746
9.8%
I 10558
8.1%
N 9481
7.3%
H 6279
 
4.8%
W 4460
 
3.4%
4121
 
3.2%
Other values (17) 21320
16.4%

VIC_AGE_GROUP
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size213.5 KiB
25-44
12281 
18-24
10086 
<18
2839 
45-64
1863 
65+
 
181
Other values (2)
 
62

Length

Max length7
Median length5
Mean length4.7832821
Min length3

Characters and Unicode

Total characters130641
Distinct characters15
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row18-24
2nd row18-24
3rd row25-44
4th row<18
5th row45-64

Common Values

ValueCountFrequency (%)
25-44 12281
45.0%
18-24 10086
36.9%
<18 2839
 
10.4%
45-64 1863
 
6.8%
65+ 181
 
0.7%
UNKNOWN 61
 
0.2%
1022 1
 
< 0.1%

Length

2025-05-28T22:02:06.582498image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-28T22:02:06.749154image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
25-44 12281
45.0%
18-24 10086
36.9%
18 2839
 
10.4%
45-64 1863
 
6.8%
65 181
 
0.7%
unknown 61
 
0.2%
1022 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
4 38374
29.4%
- 24230
18.5%
2 22369
17.1%
5 14325
 
11.0%
1 12926
 
9.9%
8 12925
 
9.9%
< 2839
 
2.2%
6 2044
 
1.6%
N 183
 
0.1%
+ 181
 
0.1%
Other values (5) 245
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 102964
78.8%
Dash Punctuation 24230
 
18.5%
Math Symbol 3020
 
2.3%
Uppercase Letter 427
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 38374
37.3%
2 22369
21.7%
5 14325
 
13.9%
1 12926
 
12.6%
8 12925
 
12.6%
6 2044
 
2.0%
0 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 183
42.9%
U 61
 
14.3%
K 61
 
14.3%
O 61
 
14.3%
W 61
 
14.3%
Math Symbol
ValueCountFrequency (%)
< 2839
94.0%
+ 181
 
6.0%
Dash Punctuation
ValueCountFrequency (%)
- 24230
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 130214
99.7%
Latin 427
 
0.3%

Most frequent character per script

Common
ValueCountFrequency (%)
4 38374
29.5%
- 24230
18.6%
2 22369
17.2%
5 14325
 
11.0%
1 12926
 
9.9%
8 12925
 
9.9%
< 2839
 
2.2%
6 2044
 
1.6%
+ 181
 
0.1%
0 1
 
< 0.1%
Latin
ValueCountFrequency (%)
N 183
42.9%
U 61
 
14.3%
K 61
 
14.3%
O 61
 
14.3%
W 61
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 130641
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 38374
29.4%
- 24230
18.5%
2 22369
17.1%
5 14325
 
11.0%
1 12926
 
9.9%
8 12925
 
9.9%
< 2839
 
2.2%
6 2044
 
1.6%
N 183
 
0.1%
+ 181
 
0.1%
Other values (5) 245
 
0.2%

VIC_SEX
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size213.5 KiB
M
24686 
F
2615 
U
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters27312
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
M 24686
90.4%
F 2615
 
9.6%
U 11
 
< 0.1%

Length

2025-05-28T22:02:06.916023image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-28T22:02:07.056772image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
m 24686
90.4%
f 2615
 
9.6%
u 11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
M 24686
90.4%
F 2615
 
9.6%
U 11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 27312
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 24686
90.4%
F 2615
 
9.6%
U 11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 27312
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 24686
90.4%
F 2615
 
9.6%
U 11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27312
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 24686
90.4%
F 2615
 
9.6%
U 11
 
< 0.1%

VIC_RACE
Categorical

Imbalance 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size213.5 KiB
BLACK
19439 
WHITE HISPANIC
4049 
BLACK HISPANIC
2646 
WHITE
 
698
ASIAN / PACIFIC ISLANDER
 
404
Other values (2)
 
76

Length

Max length30
Median length5
Mean length7.5012083
Min length5

Characters and Unicode

Total characters204873
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBLACK
2nd rowBLACK
3rd rowWHITE
4th rowWHITE HISPANIC
5th rowBLACK

Common Values

ValueCountFrequency (%)
BLACK 19439
71.2%
WHITE HISPANIC 4049
 
14.8%
BLACK HISPANIC 2646
 
9.7%
WHITE 698
 
2.6%
ASIAN / PACIFIC ISLANDER 404
 
1.5%
UNKNOWN 66
 
0.2%
AMERICAN INDIAN/ALASKAN NATIVE 10
 
< 0.1%

Length

2025-05-28T22:02:07.498520image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-28T22:02:07.649099image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
black 22085
62.7%
hispanic 6695
 
19.0%
white 4747
 
13.5%
asian 404
 
1.1%
404
 
1.1%
pacific 404
 
1.1%
islander 404
 
1.1%
unknown 66
 
0.2%
american 10
 
< 0.1%
indian/alaskan 10
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 30466
14.9%
C 29598
14.4%
L 22499
11.0%
K 22161
10.8%
B 22085
10.8%
I 19793
9.7%
H 11442
 
5.6%
7927
 
3.9%
N 7751
 
3.8%
S 7513
 
3.7%
Other values (12) 23638
11.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 196532
95.9%
Space Separator 7927
 
3.9%
Other Punctuation 414
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 30466
15.5%
C 29598
15.1%
L 22499
11.4%
K 22161
11.3%
B 22085
11.2%
I 19793
10.1%
H 11442
 
5.8%
N 7751
 
3.9%
S 7513
 
3.8%
P 7099
 
3.6%
Other values (10) 16125
8.2%
Space Separator
ValueCountFrequency (%)
7927
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 414
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 196532
95.9%
Common 8341
 
4.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 30466
15.5%
C 29598
15.1%
L 22499
11.4%
K 22161
11.3%
B 22085
11.2%
I 19793
10.1%
H 11442
 
5.8%
N 7751
 
3.9%
S 7513
 
3.8%
P 7099
 
3.6%
Other values (10) 16125
8.2%
Common
ValueCountFrequency (%)
7927
95.0%
/ 414
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 204873
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 30466
14.9%
C 29598
14.4%
L 22499
11.0%
K 22161
10.8%
B 22085
10.8%
I 19793
9.7%
H 11442
 
5.6%
7927
 
3.9%
N 7751
 
3.8%
S 7513
 
3.7%
Other values (12) 23638
11.5%

X_COORD_CD
Real number (ℝ)

High correlation 

Distinct12088
Distinct (%)44.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1009448.7
Minimum914928.06
Maximum1066815.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size213.5 KiB
2025-05-28T22:02:07.849307image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum914928.06
5-th percentile985316.88
Q11000028.5
median1007730.7
Q31016838
95-th percentile1046384.1
Maximum1066815.4
Range151887.31
Interquartile range (IQR)16809.469

Descriptive statistics

Standard deviation18377.826
Coefficient of variation (CV)0.018205805
Kurtosis2.4771539
Mean1009448.7
Median Absolute Deviation (MAD)8096.7188
Skewness-0.11683797
Sum2.7570062 × 1010
Variance3.3774448 × 108
MonotonicityNot monotonic
2025-05-28T22:02:08.032330image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1017119.438 66
 
0.2%
1026387 47
 
0.2%
1008276 47
 
0.2%
936721.6875 44
 
0.2%
1017141 44
 
0.2%
1006434 42
 
0.2%
1046405 41
 
0.2%
1008427.438 40
 
0.1%
998032 39
 
0.1%
1046367 37
 
0.1%
Other values (12078) 26865
98.4%
ValueCountFrequency (%)
914928.0625 1
< 0.1%
920262.5625 1
< 0.1%
920515.3125 1
< 0.1%
922416.3125 1
< 0.1%
922884.1875 1
< 0.1%
925479.75 1
< 0.1%
926453.875 1
< 0.1%
926731 1
< 0.1%
927599.375 1
< 0.1%
929510 1
< 0.1%
ValueCountFrequency (%)
1066815.375 1
 
< 0.1%
1066516.5 4
< 0.1%
1065539.625 2
< 0.1%
1065473.75 1
 
< 0.1%
1063056 1
 
< 0.1%
1062381 1
 
< 0.1%
1061286.375 1
 
< 0.1%
1060126.375 1
 
< 0.1%
1059864.875 1
 
< 0.1%
1059828 1
 
< 0.1%

Y_COORD_CD
Real number (ℝ)

High correlation 

Distinct12283
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean208127.4
Minimum125756.72
Maximum271127.69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size213.5 KiB
2025-05-28T22:02:08.249213image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum125756.72
5-th percentile162476
Q1182834.34
median194486.57
Q3239518.47
95-th percentile256184.25
Maximum271127.69
Range145370.97
Interquartile range (IQR)56684.133

Descriptive statistics

Standard deviation31886.378
Coefficient of variation (CV)0.15320605
Kurtosis-1.3627973
Mean208127.4
Median Absolute Deviation (MAD)22944.477
Skewness0.1665039
Sum5.6843756 × 109
Variance1.0167411 × 109
MonotonicityNot monotonic
2025-05-28T22:02:08.442006image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
183909.3438 66
 
0.2%
183623 47
 
0.2%
262634 47
 
0.2%
172119.4375 44
 
0.2%
183798 44
 
0.2%
244344 42
 
0.2%
187112.6406 41
 
0.2%
183517.7188 40
 
0.1%
175598 38
 
0.1%
186986 37
 
0.1%
Other values (12273) 26866
98.4%
ValueCountFrequency (%)
125756.7188 1
 
< 0.1%
127539 1
 
< 0.1%
128875.4141 1
 
< 0.1%
129721 1
 
< 0.1%
130418.4922 1
 
< 0.1%
131549 1
 
< 0.1%
132099.3438 1
 
< 0.1%
134246.7656 4
< 0.1%
134459 1
 
< 0.1%
135831.5156 1
 
< 0.1%
ValueCountFrequency (%)
271127.6875 2
 
< 0.1%
269635 4
< 0.1%
269534 1
 
< 0.1%
269204.8438 4
< 0.1%
269204 1
 
< 0.1%
269168.625 4
< 0.1%
269115.625 7
< 0.1%
269040 1
 
< 0.1%
268978 1
 
< 0.1%
268961 1
 
< 0.1%

Latitude
Real number (ℝ)

High correlation 

Distinct12619
Distinct (%)46.2%
Missing10
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean40.737892
Minimum40.511586
Maximum40.910818
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size213.5 KiB
2025-05-28T22:02:08.649787image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum40.511586
5-th percentile40.612606
Q140.668481
median40.700318
Q340.824067
95-th percentile40.869836
Maximum40.910818
Range0.39923252
Interquartile range (IQR)0.15558556

Descriptive statistics

Standard deviation0.08752512
Coefficient of variation (CV)0.0021484941
Kurtosis-1.3631852
Mean40.737892
Median Absolute Deviation (MAD)0.062822439
Skewness0.16654804
Sum1112225.9
Variance0.0076606466
MonotonicityNot monotonic
2025-05-28T22:02:08.849145image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.67141261 66
 
0.2%
40.67065507 47
 
0.2%
40.88745131 47
 
0.2%
40.67110691 44
 
0.2%
40.63898538 44
 
0.2%
40.83732351 42
 
0.2%
40.68004774 41
 
0.2%
40.67036569 40
 
0.1%
40.64865009 38
 
0.1%
40.67970041 37
 
0.1%
Other values (12609) 26856
98.3%
ValueCountFrequency (%)
40.51158557 1
 
< 0.1%
40.516572 1
 
< 0.1%
40.52018586 1
 
< 0.1%
40.52258031 1
 
< 0.1%
40.52443658 1
 
< 0.1%
40.527587 1
 
< 0.1%
40.52910801 1
 
< 0.1%
40.53500878 4
< 0.1%
40.535616 1
 
< 0.1%
40.53929753 1
 
< 0.1%
ValueCountFrequency (%)
40.91081809 2
 
< 0.1%
40.90666838 4
< 0.1%
40.90639492 1
 
< 0.1%
40.90548733 4
< 0.1%
40.905479 1
 
< 0.1%
40.90538927 4
< 0.1%
40.90523864 7
< 0.1%
40.90503636 1
 
< 0.1%
40.904861 1
 
< 0.1%
40.9048145 1
 
< 0.1%

Longitude
Real number (ℝ)

High correlation 

Distinct12607
Distinct (%)46.2%
Missing10
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-73.909051
Minimum-74.249303
Maximum-73.702046
Zeros0
Zeros (%)0.0%
Negative27302
Negative (%)> 99.9%
Memory size213.5 KiB
2025-05-28T22:02:09.049220image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum-74.249303
5-th percentile-73.996196
Q1-73.943027
median-73.915221
Q3-73.88233
95-th percentile-73.775916
Maximum-73.702046
Range0.54725733
Interquartile range (IQR)0.060697446

Descriptive statistics

Standard deviation0.066272257
Coefficient of variation (CV)-0.00089667309
Kurtosis2.4669825
Mean-73.909051
Median Absolute Deviation (MAD)0.029296094
Skewness-0.11919338
Sum-2017864.9
Variance0.0043920121
MonotonicityNot monotonic
2025-05-28T22:02:09.243998image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-73.88151014 66
 
0.2%
-73.84760779 47
 
0.2%
-73.91339092 47
 
0.2%
-73.88143296 44
 
0.2%
-74.17125343 44
 
0.2%
-73.91983076 42
 
0.2%
-73.7759092 41
 
0.2%
-73.91284539 40
 
0.1%
-73.95033556 38
 
0.1%
-73.77604737 37
 
0.1%
Other values (12597) 26856
98.3%
ValueCountFrequency (%)
-74.2493035 1
< 0.1%
-74.23014863 1
< 0.1%
-74.22932911 1
< 0.1%
-74.22241624 1
< 0.1%
-74.22078238 1
< 0.1%
-74.21146347 1
< 0.1%
-74.2079657 1
< 0.1%
-74.20699897 1
< 0.1%
-74.20383785 1
< 0.1%
-74.196874 1
< 0.1%
ValueCountFrequency (%)
-73.70204617 1
 
< 0.1%
-73.70308024 4
< 0.1%
-73.70659811 2
< 0.1%
-73.70686575 1
 
< 0.1%
-73.71563826 1
 
< 0.1%
-73.71803289 1
 
< 0.1%
-73.72199603 1
 
< 0.1%
-73.72653339 1
 
< 0.1%
-73.72739037 1
 
< 0.1%
-73.727478 1
 
< 0.1%
Distinct12645
Distinct (%)46.3%
Missing10
Missing (%)< 0.1%
Memory size213.5 KiB
2025-05-28T22:02:09.742782image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length45
Median length44
Mean length43.270676
Min length24

Characters and Unicode

Total characters1181376
Distinct characters20
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7665 ?
Unique (%)28.1%

Sample

1st rowPOINT (-73.73083868899994 40.662964620000025)
2nd rowPOINT (-73.92494232599995 40.81035186300006)
3rd rowPOINT (-73.91549174199997 40.74260663300004)
4th rowPOINT (-73.91945661499994 40.83778200300003)
5th rowPOINT (-73.85290950899997 40.88623791800006)
ValueCountFrequency (%)
point 27302
33.3%
73.88151014499994 66
 
0.1%
40.67141260500006 66
 
0.1%
73.84760778699996 47
 
0.1%
40.670655072000045 47
 
0.1%
73.91339091999998 47
 
0.1%
40.88745131300004 47
 
0.1%
40.67110691100004 44
 
0.1%
73.88143295699996 44
 
0.1%
74.17125343299995 44
 
0.1%
Other values (25238) 54152
66.1%
2025-05-28T22:02:10.360066image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 173561
14.7%
9 162366
13.7%
7 85042
 
7.2%
4 82827
 
7.0%
3 74639
 
6.3%
8 67516
 
5.7%
6 67117
 
5.7%
5 55260
 
4.7%
54604
 
4.6%
. 54604
 
4.6%
Other values (10) 303840
25.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 853752
72.3%
Uppercase Letter 136510
 
11.6%
Space Separator 54604
 
4.6%
Other Punctuation 54604
 
4.6%
Open Punctuation 27302
 
2.3%
Dash Punctuation 27302
 
2.3%
Close Punctuation 27302
 
2.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 173561
20.3%
9 162366
19.0%
7 85042
10.0%
4 82827
9.7%
3 74639
8.7%
8 67516
 
7.9%
6 67117
 
7.9%
5 55260
 
6.5%
1 42924
 
5.0%
2 42500
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
N 27302
20.0%
I 27302
20.0%
O 27302
20.0%
P 27302
20.0%
T 27302
20.0%
Space Separator
ValueCountFrequency (%)
54604
100.0%
Other Punctuation
ValueCountFrequency (%)
. 54604
100.0%
Open Punctuation
ValueCountFrequency (%)
( 27302
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 27302
100.0%
Close Punctuation
ValueCountFrequency (%)
) 27302
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1044866
88.4%
Latin 136510
 
11.6%

Most frequent character per script

Common
ValueCountFrequency (%)
0 173561
16.6%
9 162366
15.5%
7 85042
8.1%
4 82827
7.9%
3 74639
7.1%
8 67516
 
6.5%
6 67117
 
6.4%
5 55260
 
5.3%
54604
 
5.2%
. 54604
 
5.2%
Other values (5) 167330
16.0%
Latin
ValueCountFrequency (%)
N 27302
20.0%
I 27302
20.0%
O 27302
20.0%
P 27302
20.0%
T 27302
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1181376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 173561
14.7%
9 162366
13.7%
7 85042
 
7.2%
4 82827
 
7.0%
3 74639
 
6.3%
8 67516
 
5.7%
6 67117
 
5.7%
5 55260
 
4.7%
54604
 
4.6%
. 54604
 
4.6%
Other values (10) 303840
25.7%

Interactions

2025-05-28T22:01:59.809285image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-05-28T22:01:55.149640image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-05-28T22:01:56.183045image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-05-28T22:01:57.033138image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-05-28T22:01:57.949228image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-05-28T22:01:58.884719image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-05-28T22:01:59.949797image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-05-28T22:01:55.304168image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-05-28T22:01:56.316794image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-05-28T22:01:57.182807image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-05-28T22:01:58.099097image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-05-28T22:01:59.016164image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-05-28T22:02:00.099352image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-05-28T22:01:55.451570image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-05-28T22:01:56.450953image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-05-28T22:01:57.332879image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-05-28T22:01:58.249472image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-05-28T22:01:59.184883image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-05-28T22:02:00.248656image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-05-28T22:01:55.745724image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-05-28T22:01:56.599180image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-05-28T22:01:57.493994image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-05-28T22:01:58.416602image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-05-28T22:01:59.344507image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-05-28T22:02:00.409452image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-05-28T22:01:55.882815image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-05-28T22:01:56.749648image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-05-28T22:01:57.651053image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-05-28T22:01:58.569512image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-05-28T22:01:59.502299image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-05-28T22:02:00.560822image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-05-28T22:01:56.032533image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-05-28T22:01:56.902741image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-05-28T22:01:57.816719image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-05-28T22:01:58.732972image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-05-28T22:01:59.666502image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Correlations

2025-05-28T22:02:10.512336image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
BOROINCIDENT_KEYJURISDICTION_CODELOCATION_DESCLOC_CLASSFCTN_DESCLOC_OF_OCCUR_DESCLatitudeLongitudePERP_AGE_GROUPPERP_RACEPERP_SEXPRECINCTSTATISTICAL_MURDER_FLAGVIC_AGE_GROUPVIC_RACEVIC_SEXX_COORD_CDY_COORD_CD
BORO1.0000.0370.0770.1620.1160.0840.6030.7170.0470.1350.0330.8900.0130.0230.1490.0110.7170.603
INCIDENT_KEY0.0371.0000.0460.2871.0001.0000.0340.0110.2580.2420.365-0.0350.0170.0600.0370.0170.0110.034
JURISDICTION_CODE0.0770.0461.0000.6370.9270.1500.1280.1160.0520.0600.0000.1410.0340.0330.0690.0000.1140.127
LOCATION_DESC0.1620.2870.6371.0000.5200.4600.1250.1220.1640.1780.2460.1360.1370.0760.1290.0480.1220.125
LOC_CLASSFCTN_DESC0.1161.0000.9270.5201.0000.6410.1220.1360.1390.0940.1310.1020.2020.0620.1150.0820.1340.121
LOC_OF_OCCUR_DESC0.0841.0000.1500.4600.6411.0000.0990.0580.1900.0850.0580.0550.0970.0840.0330.0860.0620.094
Latitude0.6030.0340.1280.1250.1220.0991.0000.2620.0360.1390.035-0.5560.0200.0310.1570.0220.2601.000
Longitude0.7170.0110.1160.1220.1360.0580.2621.0000.0360.0990.0240.1840.0230.0330.1100.0061.0000.262
PERP_AGE_GROUP0.0470.2580.0520.1640.1390.1900.0360.0361.0000.4560.6830.0370.2140.1550.0600.0720.0360.036
PERP_RACE0.1350.2420.0600.1780.0940.0850.1390.0990.4561.0000.7700.1070.1330.0490.2440.0310.0990.139
PERP_SEX0.0330.3650.0000.2460.1310.0580.0350.0240.6830.7701.0000.0350.1010.0490.0380.0300.0240.035
PRECINCT0.890-0.0350.1410.1360.1020.055-0.5560.1840.0370.1070.0351.0000.0150.0260.1300.0200.185-0.556
STATISTICAL_MURDER_FLAG0.0130.0170.0340.1370.2020.0970.0200.0230.2140.1330.1010.0151.0000.0890.0480.0050.0240.019
VIC_AGE_GROUP0.0230.0600.0330.0760.0620.0840.0310.0330.1550.0490.0490.0260.0891.0000.1190.1610.0330.031
VIC_RACE0.1490.0370.0690.1290.1150.0330.1570.1100.0600.2440.0380.1300.0480.1191.0000.1860.1090.156
VIC_SEX0.0110.0170.0000.0480.0820.0860.0220.0060.0720.0310.0300.0200.0050.1610.1861.0000.0070.022
X_COORD_CD0.7170.0110.1140.1220.1340.0620.2601.0000.0360.0990.0240.1850.0240.0330.1090.0071.0000.261
Y_COORD_CD0.6030.0340.1270.1250.1210.0941.0000.2620.0360.1390.035-0.5560.0190.0310.1560.0220.2611.000

Missing values

2025-05-28T22:02:01.034520image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-28T22:02:01.491788image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-05-28T22:02:01.926902image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

INCIDENT_KEYOCCUR_DATEOCCUR_TIMEBOROLOC_OF_OCCUR_DESCPRECINCTJURISDICTION_CODELOC_CLASSFCTN_DESCLOCATION_DESCSTATISTICAL_MURDER_FLAGPERP_AGE_GROUPPERP_SEXPERP_RACEVIC_AGE_GROUPVIC_SEXVIC_RACEX_COORD_CDY_COORD_CDLatitudeLongitudeLon_Lat
022879815105/27/202121:30:00QUEENSNaN1050.0NaNNaNFalseNaNNaNNaN18-24MBLACK1.058925e+06180924.00000040.662965-73.730839POINT (-73.73083868899994 40.662964620000025)
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